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פותח על ידי קלירמאש פתרונות בע"מ -
Analysis of seasonal multispectral reflectances of small grains
Year:
1984
Source of publication :
Remote Sensing of Environment
Authors :
פוקס, מרסל
;
.
Volume :
14
Co-Authors:
Miller, G.P., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Fuchs, M., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Hall, M.J., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Asrar, G., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Kanemasu, E.T., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Johnson, D.E., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Facilitators :
From page:
153
To page:
167
(
Total pages:
15
)
Abstract:
Spectral reflectances of plant canopies combine cryptic information on the optical and geometrical characteristics of vegetation and underlying soil. A procedure was sought to objectively determine the components of multispectral reflectance data most directly related to plant and soil characteristics. The data set was measurements of reflectances in seven spectral bands (0.45-2.35 μm), collected from planting to harvest, over one bare soil, three cultivars of winter wheat, three cultivars of spring wheat, and four cultivars of spring barley at Corvallis, Oregon. A principal component analysis (PCA) executed over the entire data set determined that the distribution of the data is largely two-dimensional. However, it did not yield indices that were related to seasonal crop development. The approach was modified by first applying a PCA to the bare soil data subset only, producing a first principal component accounting for 88% of the bare soil temporal variance indicating that soil reflectance is largely one-dimensional. A PCA was then executed on the vegetation data subset under the constraint that its principal components be orthogonal to the bare soil PCA first component. The resulting first component accounted for 92% of the remaining variance and paralleled the development profile of vegetation. © 1984.
Note:
Related Files :
Agriculture
remote sensing
SEASONAL MULTISPECTRAL REFLECTANCES
SMALL GRAINS
עוד תגיות
תוכן קשור
More details
DOI :
10.1016/0034-4257(84)90012-9
Article number:
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
20670
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:38
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Scientific Publication
Analysis of seasonal multispectral reflectances of small grains
14
Miller, G.P., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Fuchs, M., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Hall, M.J., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Asrar, G., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Kanemasu, E.T., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Johnson, D.E., Department of Agronomy, Kansas State University Manhattan, KS 66506, United States
Analysis of seasonal multispectral reflectances of small grains
Spectral reflectances of plant canopies combine cryptic information on the optical and geometrical characteristics of vegetation and underlying soil. A procedure was sought to objectively determine the components of multispectral reflectance data most directly related to plant and soil characteristics. The data set was measurements of reflectances in seven spectral bands (0.45-2.35 μm), collected from planting to harvest, over one bare soil, three cultivars of winter wheat, three cultivars of spring wheat, and four cultivars of spring barley at Corvallis, Oregon. A principal component analysis (PCA) executed over the entire data set determined that the distribution of the data is largely two-dimensional. However, it did not yield indices that were related to seasonal crop development. The approach was modified by first applying a PCA to the bare soil data subset only, producing a first principal component accounting for 88% of the bare soil temporal variance indicating that soil reflectance is largely one-dimensional. A PCA was then executed on the vegetation data subset under the constraint that its principal components be orthogonal to the bare soil PCA first component. The resulting first component accounted for 92% of the remaining variance and paralleled the development profile of vegetation. © 1984.
Scientific Publication
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